TransformingTransport Big Data Europe Workshop

ERTICO-ITS Europe, Blue Tower

September, 14th 2017

Rodrigo Castiñeira (Indra) – Project Coordinator

Transport and Logistics Domain

• One of the most-used industries in the world and in EU… – 15% of GDP (source: KLU), employment of 11.2 million persons in EU-28 (source:

DG MOVE) – 3,768 billion tonne-kilometres and 6,391 billion person-kilometres in EU-28 – Key contributor to emissions: 4,824 megatonnes CO2 (source: DG MOVE)

• …and growing – Business and tourism travel expected to grow significantly over next decades – Freight transport slated to increase by 40 % in 2030 and by 80% in 2050 (source:

ALICE ETP)

• Need for paradigm shift! – A 10% efficiency improvement = EU cost savings of 100 B€ (source: ALICE ETP) – Big Data expected to lead to 500 billion USD in value worldwide in the form of

time and fuel savings, and savings of 380 megatons CO2 in transport and logistics (source: OECD)

• But: Current Situation – Only 19 % of EU transport and logistics companies employ Big Data solutions as

part of value creation and business processes, and 70% do not plan to do so in the future (source: Price Waterhouse Coopers)

Big Data Europe Workshop 2

TT in a Nutshell

• TT is a H2020 project that will demonstrate the transformations that big data will bring to the mobility and logistics sector

• TT will bring about a demonstrated increase of productivity and demonstrable impact in seven pilot areas, covering areas of major importance for the transport and logistics sector in Europe

• TT covers all transport modes and sectors TT results relevant for the whole mobility and logistics sector and market, covering EU market size of 1 305 BEUR

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Highways Airports Ports Rail

Vehicles Urban Mobility Supply Networks

Big Data Europe Workshop

Transforming Transport Pilots

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TT aims to demonstrate the transformative nature that big data technologies can bring about in the mobility and logistics sector with 13 Pilots in 7 Pilot Domains

Smart Highways Pilots

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Understand the road traffic

• Manage the traffic flow along the corridors and and the efficiency of the current infrastructure

• Improve the user experience by mitigating congestion

Optimizehighway operations

GuaranteeSafer roads

• Optimize theinfrastructuremanagement byscheduling the roadmaintenance tasks

• Optimizing the routes ofthe staff

• Reducing the number ofaccidents

• Improving the responsetime to accidents toreduce impacts ontraffic flows

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Other Pilots Objetives

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Pilot Domain Aims Smart Airport Turnaround

Reduce delays in departure flights caused by late passengers. Reduce the number of passenger missing connections and lost baggage. Improve efficiency of passenger processing stations. Reduce overall turnaround times. Obtain insight on how passenger behave along their journey. Enable fleet-wide turnaround optimization. Integrate passenger flow as well as ETA prediction models in overall smart turnaround process.

Integrated Urban Mobility

Improvement performance of the urban traffic management to better manage the traffic in the city. Improve quality of the information regarding traffic status provided to end-users. Improve last-mile logistics by providing possibility to reserve parking areas for goods delivery. Improve situational awareness of traffic flow in the city. Reduce traffic congestion in the city centre. Optimize routes and delivery times taking into account ongoing deliveries.

Dynamic Supply Networks

Explore how collaboration in e-commerce logistics improves overall distribution process performance. Identify synergies among e-commerce stakeholders for reverse logistics. Analyse current distributions to depict the distribution patterns and forecast future problems. Provide alternative shipping methods to consumers. Enable dynamically changed supply chains taking into consideration various routing and customer preference characteristics.

Other Pilots Objectives

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Pilot Domain Aims Sustainable Connected Vehicles

Proactively notify drivers / fleet managers about needed maintenance tasks of their vehicles. Inform drivers / fleet managers about tasks needed for reducing emissions and fuel consumption. Notify drivers to avoid traffic congestions.

Enable truck drivers / fleet managers to reduce buffer times and delay times at ramps/ ports. Enable planners to adapt plans according to realistic travel times / ETA-values. Provide more reliable routing results and traffic predictions.

Proactive Rail Infrastructures

Improve safety of rail workers by minimising the time spent trackside. Improve safety of rail passengers by identifying trends toward asset failure. Improve reliability of services by minimising downtime and service interruptions. Improve cost efficiency by prioritising maintenance on assets based on a number of factors. Improve service capacity by minimising disruption. Ensure at all times the level of security for which the infrastructure was designed.

Ports as Intelligent Logistics Hubs

Improve crane scheduling by calculating the optimum sequence of crane movements. Prevent unexpected failures of crane spreaders. Increase terminal efficiency by proactive management of terminal and port operations. Increase robustness of terminal processes by timely response and mitigation of problems

Project Consortium

TT consortium brings together knowledge, solutions and impact potential of major European players

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Scalability

TT 3-Stage Validation and Scale-up Methodology

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Stage Embedding Infrastructure Scale of Data

S1: Technology Validation

Problem understanding and validation of key solution ideas

Local, existing small-scale infrastructure used for exploratory experiments

Selected (historic) data pinpointing problems and opportunities

S2: Large-scale

Experiments

Controlled environments, decoupled from productive environm.

Dedicated large-scale data processing infrastructure for experimental purposes

Large historic and live data, possibly anonymized or simulated

S3: In-situ trials

Trials in the field, involving actual end-users

Actual data processing infrastructure of pilot partners

Real, live production data complemented by large scale historic data

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Data Assets

Data Assets (per pilot by the year 2020)

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Domain Vol. GB

Veloc. GB/day

Data Variety (Data Sources)

Highways ~1.000 ~1 > 20, live traffic information, historical traffic data, meteorology information, OCR data, spatial infrastructure, car sensor data, social network streams, …

Vehicles ~2.500 ~7 > 20, vehicle, position, speed, brake, black ice sensor, ABS, ESP, fuel level, emergency button, engine status, engine revolutions, tyre pressure, temperature, …

Rail ~1.500

~2 > 40, environmental, geospatial, Network Model, track circuits, axle counters, points heaters, rail temperature monitoring, overhead line tension, rail signalling power, scheduled planning and control data, track usage, …

Ports ~400.000 ~110 > 25, information about import and export of containerised, general and bulk cargo, tracking and tracing through the whole logistics chain, machines equipped with, AIS (vessel tracking), video streams of trucks and trains (from video gates), traffic management data, …

Airports ~3.000 ~30 > 35, flights scheduling, flights updates, passenger tracking information across the airport, queuing times for check-in/security/lifts, hand luggage scanning information, shops level of occupation, aircraft sensor data, …

Urban Mobility

~500 ~10.5 > 10, real time probe data from personal, freight and public transport vehicles; traffic light data and sensors, roadside camera images, usage data from public transport, …

Supply Chains

~1.500

~11 > 10, inventories, orders acquired of online retailers, deliveries, distribution information collected by 3PL/courier companies, customer delivery preferences, geographical position of customers, cost of deliveries, vehicle fill rates, …

TOTAL ~410.000 ~172 > 160 data sources

Big Data Europe Workshop http://data.transformingtransport.eu/es/ Data Catalog can be found at

Our Value!!

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Three key value dimensions for Big Data in Transport and Logistics (Source DHL/DETECON)

• Less missed connections, decreased passenger waiting times

• Delivery cost and time reductions

• …

TT covers them all!

• Oriented retailing (knowing expected preferences of passengers before arrival)

• Trip patterns for advertising, tourism, …

• Delivery mileage reduction of 5%-15%

• Rail maintenance cost reductions by up to 12%

• …

Big Data Europe Workshop

Thanks!

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 731932

Big Data Europe Workshop

Rodrigo Castiñeira R&D Programme Manager

Contact Details

Indra Sistemas S.A

Madrid, España

rcastineira@indra.es

http://www.transformingtransport.eu